When a decision tree is represented by a collection of rules, the antecedents of individual rules may contain irrelevant conditions. To avoid generating rules with irrelevant conditions, we propose a new algorithm to remove irrelevant conditions of rules in the process of converting the decision tree to rules according to information on the decision tree. Since irrelevant conditions are removed from the resultant rules, the resultant rules are more general than those represented by the decision tree. As a side effect, the resultant rules are less likely to suffer from missing branches.